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international Conference on biomedical engineering and informatics (BMEI)

作者： Jianfeng Hu Zhendong Mu Jinli Wang Institute of Information and Technology Jiang Xi Blue Sky University Nanchang China

Currently existing brain-computer interfaces (BCIs) extract feature vectors derived from amplitude information. However, they were not use the rich phase dynamics in the EEG. Phase synchronization was opposed to use for classification of motor imagery. In suitable time window, the electrodes of C3, C4 and central regions to match were selected and then Hilbert transform signal processing method was used for extracting the degree of phase synchronization between two electroencephalogram (EEC) signals by calculating the so-called phase locking value (PLV). The support vector machine (SVM) was used for classification of the motor imagery by a feature selection algorithm. It shows that the satisfactory results are obtained with single-trial accuracies of 92.5% and that synchronization differences between motor imagery depends upon frequency selection , length of data and electrode selection .

关键词： image analysis Brain computer interfaces Frequency synchronization Data mining Electrodes signal processing algorithms Support vector machines Support vector machine classification Feature extraction Electroencephalography

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Nonlinear Dynamics Techniques for the Detection of the Brain Areas Using MER signals

Nonlinear Dynamics Techniques for the Detection of the Brain...

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international Conference on biomedical engineering and informatics (BMEI)

作者： Andrea Rodríguez-Sánchez Edilson Delgado-Trejos Álvaro Orozco-Gutiérrez Germán Castellanos-Domínguez Enrique Guijarro-Estellés Technical University of Pereira Pereira Colombia Technological University of Pereira Pereira Colombia National University of Colombia Manizales Colombia Polytechnical University of Valencia Valencia España

A methodology for identifying brain areas from the brain MER signals (microelectrode recordings) is presented, which is based on a nonlinear feature set. We propose nonlinear dynamics measures such as correlation dimension, Hurst exponent and the largest Lyapunov exponent to characterize the dynamic structure. The MER records belong to the Polytechnical University of Valencia, 24 records for each zone (black substance, thalamus, subthalamus nucleus and uncertain area). The detection of each area using characteristics derived from complexity analysis was obtained through a classifier (support vector machine). The joint information between areas is remarkable and the best accuracy result was 93.75%. The nonlinear dynamics techniques help to discriminate the four brain areas considered, since they take into account the intrinsic dynamics of the signals and the structures analysis based on the multivariate statistical procedures is an important step in the data preprocessing.

关键词： Brain Microelectrodes signal analysis Nonlinear dynamical systems signal processing Neurons image reconstruction State-space methods biomedical engineering biomedical informatics

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Cardiac MRI Steam images Denoising using Bayes Classifier

Cardiac MRI Steam Images Denoising using Bayes Classifier

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Cairo international biomedical engineering Conference, CIBEC

作者： A. G. Motaal M. A. Al-Attar N. F. Osman A. S. Fahmy Center of Informatics Science School of Communication and Information technology Nile University Cairo Egypt Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Cairo Egypt

Imaging of the heart anatomy and function using magnetic resonance imaging (MRI) is an important diagnosis tool for heart diseases. Several techniques have been developed to increase the contrast-to-noise ratio (CNR) between myocardium and background. Recently, a technique that acquires cine cardiac images with black-blood contrast has been proposed. Although the technique produces cine sequence of high contrast, it suffers from elevated noise which limits the CNR. In this paper, we study the performance and efficiency of applying a Bayes classifier to remove background noise. Real MRI data is used to test and validate the proposed method; In addition, a quantitative comparison is done between the proposed method and other thresholding-based classifications techniques.

关键词： Magnetic resonance imaging image denoising Blood Myocardium Background noise signal processing biomedical engineering biomedical informatics Information technology Electronic mail

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Multichannel filter for heartbeat detection in noisy ECG recordings 10th

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10th World congress on Medical Physics and biomedical engineering, WC 2006

作者： Östlund, Nils Karlsson, M. Karlsson, S. Berglin, L. Lindecrantz, K. Sandsjö, L. Wiklund, U. Department of Biomedical Engineering and Informatics University Hospital Umeå Sweden Department of Radiation Physics Umeå University Umeå Sweden Product and Production Development Chalmers University of Technology Göteborg Sweden School of Textiles University College of Borås Borås Sweden School of Engineering University College of Borås Borås Sweden National Institute for Working Life Göteborg Sweden

ISBN: (纸本)9783540368397

In ECG signals recorded with smart clothes disturbances as intermittent loss of signal from electrodes, movement artefacts, and electromyographic interference are common. In this study a multichannel method for spatio-temporal filtering is evaluated using ECG signals from a database and with three recordings made with a T-shirt with integrated textile electrodes. The sensitivity and precision of the signals from the database were 99.6% and 98.5%, respectively, if 12 channels were used and the signal-to-noise ratio was -10 dB. The filter gave a sensitivity of 99.6% and a precision of 99.5% in the recordings from the textile electrodes. In conclusion, the results obtained indicated that multichannel spatio-temporal filtration could be a suitable method for heartbeat detection in ECG measurements with textile electrodes. © international Federation for Medical and Biological engineering 2007.

关键词： biomedical signal processing

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2008国际图像与信号处理会议征文通知

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红外 2007年第9期28卷 F0004-F0004页

The 2008 international congress on image and signal processing (CISP2008) will be held from 27 to 30 May 2008 in Sanya,Hainan,*** aim of CISP2008 is to bring together researchers working in many different areas of image and signal processing to foster exchange of new *** CISP2008 proceedings will be published by the IEEE and will be indexed in both EI and *** good papers will be recommended for publication in SCI/SCI-E indexed international ***2008 will be co-located with the 2008 international Conference on biomedical engineering and informatics (BMEI2008:http://***/BMEI2008),in order to promote cross-fertilization between the broad areas of biomedical engineering and signal processing.

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Efficient 3-D adaptive filtering for medical image enhancement

Efficient 3-D adaptive filtering for medical image enhanceme...

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2006 3rd IEEE international Symposium on biomedical Imaging: From Nano to Macro

作者： Svensson, Björn Andersson, Mats Smedby, Örjan Knutsson, Hans Dept. of Biomedical Engineering Medical Informatics Linköping University Sweden Dept. of Medicine and Care Medical Radiology Linköping University Sweden Linköping University Sweden

ISBN: (纸本)0780395778

Tensor based orientation adaptive filtering, an explicit methodology for anisotropic filtering, constitutes a flexible framework for medical image enhancement. The technique features post-filtering steer-ability and allows user interaction and direct control over the highfrequency contents of the signal. A new class of filters for local structure analysis together with filter networks significantly lowers the complexity to meet the requirements of computation time for clinical use, while maintaining accuracy. In this paper the technique is applied to low-dose CT-images, magnetic resonance angiography and T2-weighted MRI. © 2006 IEEE.

关键词： Medical imaging

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A variational framework for partially occluded image segmentation using coarse to fine shape alignment and semi-parametric density approximation

A variational framework for partially occluded image segment...

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14th IEEE international Conference on image processing, ICIP 2007

作者： Yang, Lin Foran, David J. Electrical and Computer Engineering Rutgers University Piscataway NJ 08854 United States Center of Biomedical Imaging and Informatics Cancer Institute of New Jersey UMDNJ-Robert Wood Johnson Medical School Piscataway NJ 08854 United States

ISBN: (纸本)1424414377

In this paper, we propose a variational framework which combines top-down and bottom-up information to address the challenge of partially occluded image segmentation. The algorithm applies shape priors and divides shape learning into shape mode clustering and non-rigid transformation estimation to handle intraclass and interclass coarse to fine variations. A semi-parametric density approximation using adaptive meanshift and L2E robust estimation is used to model the likelihood. A set of real images is used to show the good performance of the algorithm. © 2007 IEEE.

关键词： image segmentation

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Medical Data Mining using Particle Swarm Optimization for Temporal Lobe Epilepsy

Medical Data Mining using Particle Swarm Optimization for Te...

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congress on Evolutionary Computation

作者： M. Ghannad-Rezaie H. Soltanain-Zadeh M.-R. Siadat K.V. Elisevich Henry Ford Health System and Wayne State University Detroit MI USA Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Neurosurgery Department Henry Ford Health System Detroit MI USA

In clinical problems, numerous factors are usually involved in a medical syndrome. New advances in medicine provide a broad range of diagnosis methods to cover all aspects of a disease. However, huge amounts of raw information may confuse clinicians and decrease decision accuracy. Computerized knowledge extraction is an active area of research in medical informatics. This paper suggests a new medical data mining approach using an advanced swarm intelligence data mining algorithm. Considering medical knowledge discovery difficulties, this approach addresses common issues such as missing value management and interactive rule extraction. Here, surgery candidate selection in temporal lobe epilepsy is the main target application. However, the general idea can be applied to other medical knowledge discovery problems. Experimental results show noticeable performance improvement in the final rule-set quality while the method is flexible and fast.

关键词： Data mining Particle swarm optimization Temporal lobe Epilepsy Medical diagnostic imaging biomedical imaging Coronary arteriosclerosis Diseases Surgery Medical services

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Adaptive multirate data acquisition of 3D cell images

Adaptive multirate data acquisition of 3D cell images

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2005 IEEE international Conference on Acoustics, Speech, and signal processing, ICASSP '05

作者： Merryman, T.E. Kovačević, J. Osuna, E. Garcia Murphy, R.F. Departments of Electrical and Computer Engineering Carnegie Mellon University Center for Bioimage Informatics 5000 Forbes Ave. Pittsburgh PA 15213 United States Departments of Biomedical Engineering Carnegie Mellon University Center for Bioimage Informatics 5000 Forbes Ave. Pittsburgh PA 15213 United States Departments of Biological Sciences Carnegie Mellon University Center for Bioimage Informatics 5000 Forbes Ave. Pittsburgh PA 15213 United States

ISBN: (纸本)0780388747

We present an algorithm for efficient acquisition of fluorescence microscopy data sets, a problem not addressed until now in the literature. We do this as part of a larger system for protein classification based on their subcellular location patterns, and thus strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality;we have access only to the information that has been scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive multiresolution scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to increase either time of space resolution of our data set, all while minimally affecting the classification accuracy of the entire system. © 2005 IEEE.

关键词： image processing

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Efficient non-invasive registration with A-mode ultrasound in skull surgery

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international congress Series 2005年 1281卷 821-826页

作者： Popovic, Aleksandra Heger, Stefan Engelhardt, Martin Schmieder, Kirsten Radermacher, Klaus Helmholtz Institute for Biomedical Engineering RWTH Aachen University Aachen Germany Clinic for Neurosurgery Ruhr-University Bochum Bochum Germany

In the framework of the CRANIO project for Computer and Robot Aided Neurosurgery, a module for surgical navigation is being developed. In this contribution, we describe our approach towards an efficient registration of operating site with preoperative Computed Tomography (CT) images using 3D optical localizer system and a tracked A-mode ultrasound (US) probe. The registration plays the key role in defining the overall accuracy of the system, including robotic resection. However, apart from accuracy requirements, time efficiency, reliability and intuitiveness of the registration process are crucial for its application in clinical routine. The development of an optimal strategy comprises aspects of man-machine interaction as well as image and signal processing. We present results of our in-vitro and ex-vivo study as well as first in-vivo trials. © 2005 CARS and Elsevier B.V.

关键词： Computer-assisted skull surgery Neuronavigation Registration Robotic Ultrasound

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